SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 2151–2160 of 15113 papers

TitleStatusHype
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement LearningCode1
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot CollaborationCode0
Reinforcement Learning Problem Solving with Large Language Models—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs—0
Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies—0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
Enhancing Privacy and Security of Autonomous UAV Navigation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified